Computer-driven mapping services aid users in locating points of interest (e.g., particular buildings, addresses, and the like), among other things. Many mapping services also provide route planning applications that can suggest a fastest or most desirable route from an origin to a destination, and sometimes even provide a predicted travel time (e.g., driving time, walking time, etc.) for those routes. These predicted travel times typically represent an average (mean) travel time that can be obtained from historical trip data.
While the average travel time provides a fairly accurate prediction of travel time, it is not perfectly accurate for predicting the actual travel time. In other words, the average travel time is never going to give perfectly accurate results all of the time. At least for vehicular travel, this may be due in part to the considerable variability in driving time caused by differences in driver habits/behavior, unknown timing of traffic signals, and unobserved traffic, road, and/or weather conditions, to name only a few factors that contribute to driving time variability. Using the average travel time as a prediction of travel time does not account for the variability in travel time, which, in turn, negatively affects user experience. For instance, if the predicted travel time is underestimated, the user may be late, while if the predicted travel time is overestimated, the user may leave earlier than necessary, or may look to a third party mapping service in hopes of finding a route with a lower predicted travel time. Accordingly, a mapping service that suggests a route with a low average driving time, but high variability in driving time, is likely to result in poor user experience due to the inaccuracy of the travel time predictions.